MCM: Mamba-based Cardiac Motion Tracking using Sequential Images in MRI

• URL: http://arxiv.org/abs/2507.17678v1
• Date: Wed, 23 Jul 2025 16:40:43 GMT
• Title: MCM: Mamba-based Cardiac Motion Tracking using Sequential Images in MRI
• Authors: Jiahui Yin, Xinxing Cheng, Jinming Duan, Yan Pang, Declan O'Regan, Hadrien Reynaud, Qingjie Meng,
• Abstract summary: Myocardial motion tracking is important for assessing cardiac function and diagnosing cardiovascular diseases.<n>Many existing methods learn motion from single image pairs consisting of a reference frame and a randomly selected target frame from the cardiac cycle.<n>We propose a novel Mamba-based cardiac motion tracking network (MCM) that explicitly incorporates target image sequence from the cardiac cycle.
• Score: 5.534557831834127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Myocardial motion tracking is important for assessing cardiac function and diagnosing cardiovascular diseases, for which cine cardiac magnetic resonance (CMR) has been established as the gold standard imaging modality. Many existing methods learn motion from single image pairs consisting of a reference frame and a randomly selected target frame from the cardiac cycle. However, these methods overlook the continuous nature of cardiac motion and often yield inconsistent and non-smooth motion estimations. In this work, we propose a novel Mamba-based cardiac motion tracking network (MCM) that explicitly incorporates target image sequence from the cardiac cycle to achieve smooth and temporally consistent motion tracking. By developing a bi-directional Mamba block equipped with a bi-directional scanning mechanism, our method facilitates the estimation of plausible deformation fields. With our proposed motion decoder that integrates motion information from frames adjacent to the target frame, our method further enhances temporal coherence. Moreover, by taking advantage of Mamba's structured state-space formulation, the proposed method learns the continuous dynamics of the myocardium from sequential images without increasing computational complexity. We evaluate the proposed method on two public datasets. The experimental results demonstrate that the proposed method quantitatively and qualitatively outperforms both conventional and state-of-the-art learning-based cardiac motion tracking methods. The code is available at https://github.com/yjh-0104/MCM.

Related papers

• Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge [56.28872161153236]
  Deep learning models have achieved state-of-the-art performance in automated Cardiac Magnetic Resonance (CMR) analysis.<n>The efficacy of these models is highly dependent on the availability of high-quality, artifact-free images.<n>To promote research in this domain, we organized the MICCAI CMRxMotion challenge.
  arXiv  Detail & Related papers  (2025-07-25T11:12:21Z)
• Dyna3DGR: 4D Cardiac Motion Tracking with Dynamic 3D Gaussian Representation [23.163504377816043]
  We propose Dynamic 3D Gaussian Representation (Dyna3DGR), a novel framework that combines explicit 3D Gaussian representation with implicit neural motion field modeling.<n>Our method simultaneously optimize cardiac structure and motion in a self-supervised manner, eliminating the need for extensive training data or point-to-point correspondences.
  arXiv  Detail & Related papers  (2025-07-22T14:06:50Z)
• EchoWorld: Learning Motion-Aware World Models for Echocardiography Probe Guidance [79.66329903007869]
  We present EchoWorld, a motion-aware world modeling framework for probe guidance.<n>It encodes anatomical knowledge and motion-induced visual dynamics.<n>It is trained on more than one million ultrasound images from over 200 routine scans.
  arXiv  Detail & Related papers  (2025-04-17T16:19:05Z)
• Recovering Pulse Waves from Video Using Deep Unrolling and Deep Equilibrium Models [45.94962431110573]
  Camera-based monitoring of vital signs, also known as imaging photoplethysmography (i), has seen applications in driver-monitoring, affective computing, and more.<n>We introduce methods that combine signal processing and deep learning methods in an inverse problem.
  arXiv  Detail & Related papers  (2025-03-21T16:11:21Z)
• Bidirectional Recurrence for Cardiac Motion Tracking with Gaussian Process Latent Coding [9.263168872795843]
  GPTrack is a novel unsupervised framework crafted to explore the temporal and spatial dynamics of cardiac motion. It enhances motion tracking by employing the sequential Gaussian Process in the latent space and encoding statistics by spatial information at each time stamp. Our GPTrack significantly improves the precision of motion tracking in both 3D and 4D medical images while maintaining computational efficiency.
  arXiv  Detail & Related papers  (2024-10-28T05:33:48Z)
• Highly efficient non-rigid registration in k-space with application to cardiac Magnetic Resonance Imaging [10.618048010632728]
  We propose a novel self-supervised deep learning-based framework, dubbed the Local-All Pass Attention Network (LAPANet) for non-rigid motion estimation. LAPANet was evaluated on cardiac motion estimation across various sampling trajectories and acceleration rates. The achieved high temporal resolution (less than 5 ms) for non-rigid motion opens new avenues for motion detection, tracking and correction in dynamic and real-time MRI applications.
  arXiv  Detail & Related papers  (2024-10-24T15:19:59Z)
• LaMoD: Latent Motion Diffusion Model For Myocardial Strain Generation [5.377722774297911]
  We introduce a novel Latent Motion Diffusion model (LaMoD) to predict highly accurate DENSE motions from standard CMR videos.<n> Experimental results demonstrate that our proposed method, LaMoD, significantly improves the accuracy of motion analysis in standard CMR images.
  arXiv  Detail & Related papers  (2024-07-02T12:54:32Z)
• Semantic-aware Temporal Channel-wise Attention for Cardiac Function Assessment [69.02116920364311]
  Existing video-based methods do not pay much attention to the left ventricular region, nor the left ventricular changes caused by motion. We propose a semi-supervised auxiliary learning paradigm with a left ventricular segmentation task, which contributes to the representation learning for the left ventricular region. Our approach achieves state-of-the-art performance on the Stanford dataset with an improvement of 0.22 MAE, 0.26 RMSE, and 1.9% $R2$.
  arXiv  Detail & Related papers  (2023-10-09T05:57:01Z)
• Motion Magnification in Robotic Sonography: Enabling Pulsation-Aware Artery Segmentation [44.868281669589194]
  In order to improve the artery segmentation accuracy and stability during scans, this work presents a novel pulsation-assisted segmentation neural network (PAS-NN) Motion magnification techniques are employed to amplify the subtle motion within the frequency band of interest to extract the pulsation signals from sequential US images. The extracted real-time pulsation information can help to locate the arteries on cross-section US images.
  arXiv  Detail & Related papers  (2023-07-07T16:14:17Z)
• A kinetic approach to consensus-based segmentation of biomedical images [39.58317527488534]
  We apply a kinetic version of a bounded confidence consensus model to biomedical segmentation problems. The large time behavior of the system is then computed with the aid of a surrogate Fokker-Planck approach. We minimize the introduced segmentation metric for a relevant set of 2D gray-scale images.
  arXiv  Detail & Related papers  (2022-11-08T09:54:34Z)
• Mesh-based 3D Motion Tracking in Cardiac MRI using Deep Learning [11.177851736773823]
  3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and diagnosis of cardiovascular diseases. Most of the previous methods focus on estimating pixel-/voxel-wise motion fields in the full image space. In this work, we model the heart as a 3D geometric mesh and propose a novel deep learning-based method that can estimate 3D motion of the heart mesh from 2D short-axis CMR images.
  arXiv  Detail & Related papers  (2022-09-05T15:10:27Z)
• DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images [10.434681088538866]
  We propose a novel deep learning-based fully unsupervised method for in vivo motion tracking on t-MRI images. Our method has been validated on a representative clinical t-MRI dataset.
  arXiv  Detail & Related papers  (2021-03-04T00:42:11Z)
• Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation [51.72616167073565]
  We propose Motion Pyramid Networks, a novel deep learning-based approach for accurate and efficient cardiac motion estimation. We predict and fuse a pyramid of motion fields from multiple scales of feature representations to generate a more refined motion field. We then use a novel cyclic teacher-student training strategy to make the inference end-to-end and further improve the tracking performance.
  arXiv  Detail & Related papers  (2020-06-28T21:03:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.